Fr. 188.00

Topics on Methodological and Applied Statistical Inference

English · Hardback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more

This book brings together selected peer-reviewed contributions from various research fields in statistics, and highlights the diverse approaches and analyses related to real-life phenomena. 
Major topics covered in this volume include, but are not limited to, bayesian inference, likelihood approach, pseudo-likelihoods, regression, time series, and data analysis as well as applications in the life and social sciences.
The software packages used in the papers are made available by the authors.
This book is a result of the 47th Scientific Meeting of the Italian Statistical Society, held at the University of Cagliari, Italy, in 2014.

List of contents

Introducing Prior Information into the Forward Search for Regression.- A Finite Mixture Latent Trajectory Model for Hirings and Separations in the Labor Market.- Outliers in Time Series - An Empirical Likelihood Approach.- Advanced Methods to Design Samples for Land Use/Land Cover Surveys.- Heteroscedasticity, Multiple Populations and Outliers in Trade Data.- How to Marry Robustness and Applied Statistics.- Logistic Quantile Regression to Model Cognitive Impairment in Sardinian Cancer Patients.- Bounding the Probability of Causation in Mediation Analysis.- Analysis of Collaboration Structures though Time - The Case of Technological Districts.- Bayesian Spatio-temporal Modeling of Urban Air Pollution Dynamics.- Clustering Functional Data on Convex Function Spaces.- The Impact of Demographic Change on Sustainability of Emergency Departments.- Bell Shaped Fuzzy Numbers Associated With the Normal Curve.- Improving Co-authorship Network Structures by Combining Heterogeneous Data Sources.- Statistical Issues in Bayesian Meta-Analysis.- Statistical Evaluation of Forensic DNA Mixtures from Multiple Traces.- A Note on Semivariogram.- Geographically Weighted Regression Analysis of Cardiovascular Diseases - Evidence From Canada Health Data.- Pseudo-Likelihoods for Bayesian Inference. 

About the author

The three editors are researchers well known to national and international statistical community. In addition, each of them has a large scientific production and the results of their research are published in numerous journals well classified.

Summary

This book brings together selected peer-reviewed contributions from various research fields in statistics, and highlights the diverse approaches and analyses related to real-life phenomena. 
Major topics covered in this volume include, but are not limited to, bayesian inference, likelihood approach, pseudo-likelihoods, regression, time series, and data analysis as well as applications in the life and social sciences.
The software packages used in the papers are made available by the authors.
This book is a result of the 47th Scientific Meeting of the Italian Statistical Society, held at the University of Cagliari, Italy, in 2014.

Product details

Assisted by Tonio di Battista (Editor), Tonio Di Battista (Editor), Elía Moreno (Editor), Elías Moreno (Editor), Walter Racugno (Editor)
Publisher Springer, Berlin
 
Languages English
Product format Hardback
Released 01.01.2016
 
EAN 9783319440927
ISBN 978-3-31-944092-7
No. of pages 220
Dimensions 167 mm x 243 mm x 18 mm
Weight 508 g
Illustrations X, 220 p. 55 illus., 37 illus. in color.
Series Studies in Theoretical and Applied Statistics
Selected Papers of the Statistical Societies
Selected Papers of the Statistical Societies
Studies in Theoretical and Applied Statistics
Subject Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.